Enhancement of the Diversity of Pollinators and Beneficial Insects in Intensively Managed Vineyards

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Article Enhancement of the Diversity of Pollinators and Beneﬁcial Insects in Intensively Managed Vineyards

Francisco Javier Peris-Felipo 1,* , Fernando Santa 1 , Oscar Aguado 2, José Vicente Falcó-Garí 3, Alicia Iborra 3, Michael Schade 1, Claire Brittain 4, Vasileios Vasileiadis 1 and Luis Miranda-Barroso 5

1 Syngenta Crop Protection, Rosentalstrasse 67, 4058 Basel, Switzerland; [email protected] (F.S.); [email protected] (M.S.); [email protected] (V.V.) 2 Andrena Iniciativas y Estudios Medioambientales S.L., Calle Gabilondo 16bis, 47007 Valladolid, Spain; [email protected] 3 Laboratory of Entomology and Pest Control, Institute Cavanilles of Biodiversity and Evolutionary Biology, Calle Catedrático José Beltrán 2, Paterna, 46980 Valencia, Spain; [email protected] (J.V.F.-G.); [email protected] (A.I.) 4 Jeallot’s Hill International Research Centre, Syngenta, Bracknell RG42 6EY, UK; [email protected] 5 Agricultura Sostenible Syngenta España, Calle de la Ribera del Loira, 8, 10, 28042 Madrid, Spain; [email protected] * Correspondence: [email protected]; Tel.: +41-766-908-032

Simple Summary: The continuous intensification of agricultural production has resulted in higher yields and more yield security. However, these achievements went along with the substitution of heterogeneous agricultural landscapes by homogeneous ones with poor crop diversity, short crop Citation: Peris-Felipo, F.J.; Santa, F.; rotations, and thanks to the high efficacy of modern herbicides and also to minimum in-crop diversity. Aguado, O.; Falcó-Garí, J.V.; Iborra, A.; Schade, M.; Brittain, C.; A severe increase in plot size led to the elimination of ecologically valuable structural elements that Vasileiadis, V.; Miranda-Barroso, L. had provided floral resources and nesting sites. Over the few last decades, several studies have been Enhancement of the Diversity of conducted to try to find solutions against insect decline and to preserve biodiversity. In the present Pollinators and Beneficial Insects in study, the integration of cover plants between the lines of the vineyards to enhance biodiversity Intensively Managed Vineyards. is shown. The benefits of the cover plants use are presented based on the results achieved on five Insects 2021, 12, 740. intensive wine farms in Spain. Our findings suggest that the use of cover plants provide a wide range https://doi.org/10.3390/insects12080740 of enhancements in the insect community with a significant increase both in the number of species and the number of individuals showing an important influence over time, which would tend to have Academic Editors: Giovanni Burgio, a significant conservation impact thanks to its effect as a reservoir of species. David G. James, Daniele Sommaggio and Andrea Lucchi Abstract: (1) Modern, intensive agricultural practices have been attributed to the loss of insect biodiversity and abundance in agroecosystems for the last 80 years. The aim of this work is to test Received: 29 May 2021 Accepted: 16 August 2021 whether there are statistically significant differences in insect abundance between different zones Published: 18 August 2021 and over time on the vineyard field. (2) The study was carried out in five intensive wine farms in Spain over a three-year period (2013–2015). Each field was divided into two zones, one where

Publisher’s Note: MDPI stays neutral cover plants were planted, and another remained unchanged (without cover). (3) A clear trend to with regard to jurisdictional claims in increase the average number of insect species and individuals throughout the years in all farms was published maps and institutional afﬁl- observed. Moreover, the zones with cover plants showed a signiﬁcant difference with respect to the iations. zones without. (4) The use of permanent cover plants allows creating areas of refuge for the insects favouring their conservation and reducing the agriculture impact in the insect decline.

Keywords: biodiversity; vineyards; agro-ecosystems; sustainability; habitat management; insect Copyright: © 2021 by the authors. conservation; cover plants; natural enemies; pollinators Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons 1. Introduction Attribution (CC BY) license (https:// Since World War II, governments have been supporting farmers to increase their creativecommons.org/licenses/by/ productivity despite the increased costs of production and its environmental impact. Rapid 4.0/).

Insects 2021, 12, 740. https://doi.org/10.3390/insects12080740 https://www.mdpi.com/journal/insects Insects 2021, 12, 740 2 of 12

intensification and industrialization of agriculture are often cited as contributing factors in insect declines [1–5]. Land-use changes in the 1950–1970s notably impacted modern intensive agricultural practices, resulting in a substitution of heterogeneous agricultural landscapes by homogeneous ones. A big increase in farm size led to an elimination of edges and other ecologically valuable structural elements that had provided floral resources and nesting sites [4,6–10]. Habitat loss caused vital changes in the natural communities of birds, insects and mammals. In addition, the intensification measures have also led to soil quality losses [3,11–14]. Pollinating insects have been highlighted as being severely affected by agricultural intensification [15–22]. Within the pollinating insects, wild bees are often the group of insects that suffered the highest decline, reaching up to 50% of populations [19,23–25]. Over the last decades, several options were considered to reverse the situation. The use of multifunctional margins to increase the abundance of wildflowers, insects and birds has been highlighted as an important way of promoting nature conservation [3,5,26–30]. However, most of these studies are based on the study of bees or bumble bees [10,22,31–34], and only a few included all the insect groups [13,35]. In this work, assuming that biodiversity can be measured by the abundance of insects, the differences in several environments are evaluated and verified if their effect is permanent over time. This leads to testing two hypotheses. First, there is a benefit of integrating the cover plants in the inter-row lines of the crop to enhance biodiversity. Second, the use of cover plants improves biodiversity over time. These hypotheses were studied in five intensive wine farms in Spain.

2. Materials and Methods 2.1. Areas of Study For this study, ﬁve commercial farms were selected in Spain (Table1). All the established agricultural practices in these farms, such as tilling, fertilization, and phytosanitary treatments, remained unchanged. Management measures were conﬁned to the crop, trying not to interfere with the cover plants.

Table 1. Farm location and GPS information.

Province Place GPS Coordinates Grape Variety Field Area (ha) 41◦37022.5100 N, Burgos Aranda de Duero Tempranillo 5 3◦41017.4400 W 40◦18000.6100 N, Madrid Villamanta Garnacha 4 4◦07017.8300 W 39◦49034.7000 N, Toledo El Carpio de Tajo Tempranillo 5 4◦26038.8000 W 39◦59036.0000 N, Toledo Otero Tempranillo 4 4◦31042.6000 W 41◦37001.0000 N, Valladolid Sardón de Duero Tempranillo 4.5 4◦24039.0000 W

2.2. Mixture Plant Selection The selection of plant species was based on several fundamental criteria such as the strict use of native species, ensuring a smooth climatic adaptation; being non-weed for the crop; featuring easy maintenance and capacity for self-sowing, as well as staggered flowering phenologies; and finally, being attractive for pollinators and natural enemies. The cover plants were established using an herbaceous mixture consisting of Borago officinalis L. (10%), Calendula officinalis L. (22.5%), Coriandrum sativus L. (10%), Diplotaxis catholica (L.) DC. (5%), Echium vulgare L. (5%), Melilotus officinalis (L.) Pall. (12.5%), Nigella damascena (L.) (5%), Salvia verbenaca L. (10%), Silene vulgaris (Moench) Garcke (10%), and Vicia sativa L. (10%). This mixture was sown by an electric drill with air distribution after the soil preparation by flail mower and subsequent covering of the seed with a drag. The Insects 2021, 12, 740 3 of 12

sowing dose used was 15 Kg/Ha. The cover plants were mowed in autumn and then left to regrow.

2.3. Experimental Design and Sampling To investigate the dynamics of effects of cover plants on insect biodiversity, the experiment was conducted for 3 years (2013–2015). From each farm, a field was selected, and this field was divided into two zones separated by 500–600 m. In zone 1 (with) three lines of cover plants were planted, while zone 2 (without) was kept without cover plants or weeds following the farming practices (application of residual herbicide at the beginning of the season and mechanical maintenance of weeds during the season to maintain the field without spontaneous vegetation). The insect abundance was assessed visually and by sweeping net (observed and captured specimens were merged to perform the corresponding analyses). The observations were done by moving in a zigzag along fixed transects of 50 × 2 m during 15 min per line and 4 times per day to avoid the light and temperature gradient and obtain a more representative sample. The samples were carried out four times per year based on the phenological state of the crop (leaf growth, flowering, veraison, and harvest). The collected specimens were preserved in cyanide to keep them intact and to avoid discoloration. All the specimens were identified to genus level using appropriate entomological literature (see [36–50]). Specimens are deposited in the entomological collection of the National Museum of Natural Sciences (Madrid, Spain; MNCN).

2.4. Data Analysis An approach based on using statistical data analysis was developed to study the total number of insects as a measure of their biodiversity. To do that, we initially perform an exploratory data analysis of the counts of insects, comparing them among zones and between years to study their behaviour, identify abnormal situations, and detect patterns in the data. This analysis incorporates a plot of means of the number of insects between farms, zones, and years. We also build a scatterplot matrix of the counts across years to evaluate their structure of temporal autocorrelation. We then ﬁt a generalised linear mixed model (GLMM) for count data. Mixed models allow including explicitly as sources of variability spatial and temporal factors [51] that affect the dynamics of the biodiversity. The model is speciﬁed as follows

 i = 1, 2   j = 1, 2, 3 g n = µ + α + β + (αβ) + γ + δ + ε (1) ijkl i j ij k l ijkl k = 1, 2, 3, 4, 5   l = 1, 2, ··· , 142

where nijkl represents the count of insects in ith zone, jth year, kth farm, and lth genus, and g is a monotonous function that linearises the relationship between the response variable and the systematic component of the model. Under the scope of count data, it is common to assume the nijkl follows a Poisson distribution when its mean and variance are equal or a negative binomial when its variance is greater than its mean (overdispersion) [52]. Here, αi is the zone (fixed effect), βj is the year (fixed effect), (αβ)ij is the interaction between zone and year (fixed effect), γk is the farm (random effect), and δl is the genus (random effect). The farm effect (γk) accounts for the spatial variability and the grape variety. The genus effect (δl) represents the unfixed number of species per genera and helps to reduce the common overdispersion in count data due to a significant amount of zero counts. The parameters of the model in Equation (1) were estimated via maximum likelihood and assuming two possible distributions for the response variable. The fitted models are compared to choose the best probability distribution of the response variable by using a likelihood ratio (LR) contrast [53]. Statistical data analysis is performed by using R statistical software [54]. Insects 2021, 12, 740 4 of 12

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3. Results 3.3.1. Results Diversity of Insects 3.1. DiversityDuring of the Insects three-year research programme, a total of 7950 insects belonging to Coleoptera (1583), Diptera (1440), Hymenoptera (2291), Lepidoptera (2300), and Neu- ropteraDuring (336) the were three-year collected. research Of them, programme, 3308 individuals a total of (125 7950 genera) insects were belonging captured to Cole- from opterathe area (1583), without Diptera cover (1440), plants Hymenoptera and 4642 (139 (2291), genera) Lepidoptera from the area (2300), with. and All Neuroptera the farms (336)showed were a greatercollected. number Of them, of genera 3308 individual and individualss (125 genera) in the areaswere withcaptured cover from plants the thanarea without (Tablecover 2plants). Similar and results 4642 (139 were genera) observed from when the analysingarea with. the All abundance the farms ofshowed genera a greaterin order number in each of thegenera sampling and individuals areas and treatmentin the areas zone with (Figure cover1 ).plants The completethan without list (Tableof genera 2). Similar found inresults each locality,were observed year, and when study analysing zone (with the abundance and without of covergenera plants) in order is inprovided each of inthe Supplementary sampling areas Materials. and treatment zone (Figure 1). The complete list of genera found in each locality, year, and study zone (with and without cover plants) is provided Table 2. Thein Supplementary abundance of genera Materials. and individuals by vineyard farm and treatment.

Aranda de DueroTable El2. CarpioThe abundance de Tajo of genera and Otero individuals by Sardvineyardón de farm Duero and treatment. Villamanta

with without with Arandawithout de Duerowith El Carpiowithout de Tajo with Otero without Sardón de Duerowith Villamantawithout Cover Cover Cover Cover Cover Cover Cover Cover Cover Cover with without with without with without with without with without

Genera 86 78 75Cover 72 Cover 82Cover Cover 74 Cover 106 Cover Cover 91 Cover 101 Cover 85Cover Individuals 1053 899Genera 552 86508 78 1100 75 679 72 82 1211 74 707106 91 726101 51585 Individuals 1053 899 552 508 1100 679 1211 707 726 515

Figure 1. The abundance of genera by locality and treatment zone by insect order.

The percentages of insect species when comparingcomparing the zone with cover plants and without always were greater in the zone with plants, being the average average increase increase of 12.10%, 12.10%, having the lowest value in Carpio by only 4.16% and the greatest increase in Villamanta (18.82%). SimilarSimilar resultsresults were were obtained obtained when when considering considering individuals, individuals, with with the mean the mean value valueof 40.01%, of 40.01%, obtaining obtaining again theagain lowest the lowest value invalue Carpio in Carpio (8.66%) (8.66%) and the and highest the highest in Sard óinn (71.28%).Sardón (71.28%). The analysis analysis of of genera genera identified identified in inareas areas with with and and without without cover cover plants plants showed showed that areasthat areas with withplants plants had a hadhigher a highernumber number of genu ofs than genus the than area thewithout area without(Table 2). (Table The five2). mostThe five frequent most frequentgenus in genusall farms in all were farms Apis were, ChrysoperlaApis, Chrysoperla, Coccinella,, Coccinella, Eristalis, Eristalisand Andrena, and Andrena(Table 3).(Table All these3). All genera these were genera also were the also most the abundant most abundant in areas in with areas cover with plants, cover plants, where wherethey represented they represented 27.18% 27.18% of the oftotal the individu total individuals.als. In areas In areaswithout without plants, plants, the first the four first four genera previously listed were also the most captured, accompanied by Sphaerophoria,

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representinggenera previously a total listed of 24.12%. were Thealso otherthe most genus captured, (134 in zoneaccompanied 1 and 120 by in Sphaerophoria zone 2) had a, veryrep- lowresenting frequency a total of of abundance. 24.12%. The other genus (134 in zone 1 and 120 in zone 2) had a very low frequency of abundance. Table 3. Top 5 most abundant genera in areas with and without cover plants. Table 3. Top 5 most abundant genera in areas with and without cover plants. With Cover Plants Without Cover Plants Genuswith Individuals Cover Plants % Genus without Individuals Cover Plants % Genus Individuals % Genus Individuals % CoccinellaCoccinella 301301 6.356.35 CoccinellaCoccinella 235235 7.107.10 ApisApis 285285 6.016.01 EristalisEristalis 153153 4.634.63 EristalisEristalis 268268 5.655.65 SphaerophoriaSphaerophoria 147147 4.444.44 AndrenaAndrena 223223 4.704.70 ApisApis 141141 4.264.26 ChrysoperlaChrysoperla 212212 4.474.47 ChrysoperlaChrysoperla 122122 3.693.69 TotalTotal 12891289 27.1827.18 798798 24.1224.12

The biology of these genera shows two clearclear keykey functionsfunctions withinwithin thethe agriculturalagricultural ecosystem. ApisApis andand AndrenaAndrena carrycarry outout functions functions of pollination, of pollination, while while CoccinellaCoccinella and Chrys-and Chrysoperlaoperla are predatorsare predators of aphids of aphids whose whose behavior behavior regulates regulates aphid aphid populations. populations. In addition, In addition,genera such genera as Eristalis such as andEristalis Sphaerophoriaand Sphaerophoria perform bothperform functions both (pollination functions (pollination and preda- andtion). predation).

3.2. GLMM Modelling An exploratory data analysis was initially performed to describe the behaviour ofof the total numbernumber ofof insectsinsects under under the the assessed assessed determinants. determinants. Figure Figure2 shows 2 shows the changesthe changes in the in averagethe average number number of insects of insects between between zones, zones, farms, farms, and and years. years. There There is a is clear a clear trend trend of the of averagethe average number number of insects of insects increasing increasing through through the years the years in all in farms. all farms. However, However, this trend this doestrend notdoes seem not toseem be theto be same the betweensame between farms, farms, which which differs differs the rate the of rate change. of change. In most In cases,most cases, the zones the zones with cover with plantscover haveplants a higherhave a numberhigher number of insects of in insects comparison in comparison with the zoneswith the without zones without cover plants. cover Additionally,plants. Additional therely, is there an interaction is an interaction effect among effect among the zones the andzones the and years. the years.

Figure 2. Plot of the means of of the the total total number number of of insects insects between between zones zones across across the the farms farms through through the years.

Additionally, a study of the structure of temporal correlation between the count of insects through the years was conducted. To do that, we plotted a scatterplot matrix

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Additionally, a study of the structure of temporal correlation between the count of insects through the years was conducted. To do that, we plotted a scatterplot matrix among amongthe total the number total number of insects of insects in each in observed each obse yearrved and year tested and thetested statistical the statistical significance signifi- of cancePearson’s of Pearson’s correlation correlation coefficient coefficient of the counts of th amonge counts years. among Figure years.3 presents Figure 3 the presents results the of resultsthe analysis of the and analysis indicates and that indicates the number that the of insectsnumber is of significantly insects is significantly correlated between correlated the betweenyears. The the funnel-shaped years. The funnel-shaped clouds of points clouds also of reveal points an also effect reveal of unequal an effect variability of unequal of variabilitythe number of of the insects number between of insects years between that talks years about that thetalks complexity about the ofcomplexity insect population of insect populationdynamics among dynamics the contrastingamong the co farmingntrasting environments. farming environments.

Figure 3. Scatterplot matrix of the total number of insects through the years. Figure 3. Scatterplot matrix of the total number of insects through the years. We fitted two-count GLMMs based on Equation (1) by considering a Poisson and a We fitted two-count GLMMs based on Equation (1) by considering a Poisson and a negative binomial response. Table4 presents the statistics for the goodness of fit to the negative binomial response. Table 4 presents the statistics for the goodness of fit to the estimated models in each case. The LR test shows that the model has a better fit using a estimated models in each case. The LR test shows that the model has a better fit using a negative binomial distribution for the response variable since it rejects the null hypothesis negative binomial distribution for the response variable since it rejects the null hypothesis that the ratio is less than 1 in the case of the Poisson distribution, which means that the thatvariance the ratio of the is countless than of insects 1 in the increases case of the more Poisson rapidly distribution, than their mean which and means the negativethat the variancebinomial of distribution the count of is insects more accurate increases as more a probabilistic rapidly than schema their formean the and total the number negative of binomialinsects. Moreover,distribution the is othermore statisticsaccurate ofas thea probabilistic goodness of schema fits, such for asthe AIC total and number BIC, are of insects.considerably Moreover, lower the for other the model statistics that assumesof the goodness the negative of fits, binomial such as distribution AIC and BIC, for are the considerablyresponse variable. lower And, for the finally, model we that concluded assumes that the the negative preferred binomial model isdistribution suitable to explainfor the responsethe total numbervariable. of And, insects finally, as a function we concluded of the examined that the preferred systematic model component is suitable (i.e., zones,to ex- plainyears, the farms, total and number genera) of insects since the as a deviance function statistic of the examined is also statistically systematic significant. component (i.e., zones, years, farms, and genera) since the deviance statistic is also statistically significant. Table 4. Statistics of goodness of fit for fitted count regression models. Table 4. Statistics of goodness of fit for fitted count regression models. Test Poisson Negative Binomial Test Poisson Negative Binomial LikelihoodLikelihood ratio ratio (LR) (LR) 2.29 0.75 0.75 *** DevianceDeviance (D) (D) 15,453.7 15453.7 *** *** 11,598.6 11598.6 *** *** AIC 15,469.715469.7 11,616.611616.6 BIC 15,520.515520.5 11,673.811673.8 *** [0,[0, 0.001];0.001]; ** ** [0.001, [0.001, 0.01]; 0.01]; * [0.01, * [0.01, 0.05]; 0.05];· [0.05, ∙ [0.05, 0.1]; [0.1, 0.1]; 1]. [0.1, 1]

Table 5 presents the Analysis of Deviance, and Table 6 presents the summary of the estimation for the selected negative binomial GLMM, respectively. The results show that the parameters related to fixed effects are all statistically significant. This latter means: (i) there is an interaction effect between the zone and the year that implies that the main effect of the zone is not constant through the years, and their signs are positive, which tells that increase in the average of the total number of insects in both zones through time; (ii)

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Table5 presents the Analysis of Deviance, and Table6 presents the summary of the estimation for the selected negative binomial GLMM, respectively. The results show that the parameters related to ﬁxed effects are all statistically signiﬁcant. This latter means: (i) there is an interaction effect between the zone and the year that implies that the main effect of the zone is not constant through the years, and their signs are positive, which tells that increase in the average of the total number of insects in both zones through time; (ii) there are also differences due to both main effects, an increase of the number of insects with time and a smaller number of insects in zones without cover plants.

Table 5. Analysis of Deviance Table (Type II Wald chi-square tests) in the ﬁtted negative binomial regression model for the total number of insects.

Source Chisq Df p-Value Zone 56.04 1 7.117 × 10−14 *** Year 412.57 1 <2.2 × 10−16 *** Zone:Year 42 2 7.553 × 10−10 *** *** [0, 0.001]; ** [0.001, 0.01]; * [0.01, 0.05]; · [0.05, 0.1]; [0.1, 1].

Table 6. The estimated regression coefﬁcients in the ﬁtted negative binomial regression model for the total number of insects.

Random Effects Groups Name Variance Std. Dev. Genus (Intercept) 2.40 1.55 Farm (Intercept) 0.06 0.25 Fixed Effects Parameter Estimate Std. Error (Intercept) −0.78 0.19 *** Zone:without −0.90 0.10 *** Year:2014 0.32 0.09 *** Year:2015 1.07 0.08 *** Zone:without-Year: 0.84 0.13 *** 2014 Zone:without-Year: 0.58 0.13 *** 2015 *** [0, 0.001]; ** [0.001, 0.01]; * [0.01, 0.05]; · [0.05, 0.1]; [0.1, 1].

We finally performed hypotheses testing over the fixed and random effects to under- stand the behaviour of the total number of insects under the examined factors. For the fixed effects (zones and years), Tukey multiple comparisons strategy was used to compare the differences among the zones without and with cover plants through the years. Thus, for each year, we tested the null hypothesis that there is not a difference in the mean of the number of insects between the zone without cover plants and the zone with cover plants. The results are shown in Figure4a and indicate that in the years 2013 and 2015, there was a statistically significant difference, where the zone with cover plants had more insects than the zone without cover plants on average. Regarding the random effect associated with the farms, the estimated parameters, related to the random intercepts (see Figure4b), show that Carpio and Sard ón were statistically significant contrasting environments. Sardón had a higher total number of insects while Carpio had a lower count of insects. The other three farms did not show significant differences since the confidence intervals for the random intercepts contain the value zero. Insects 2021, 12, x FOR PEER REVIEW 7 of 12

there are also differences due to both main effects, an increase of the number of insects with time and a smaller number of insects in zones without cover plants.

Table 5. Analysis of Deviance Table (Type II Wald chi-square tests) in the fitted negative binomial regression model for the total number of insects.

Source Chisq Df p-Value Zone 56.04 1 7.117e–14 *** Year 412.57 1 <2.2e–16 *** Zone:Year 42 2 7.553e–10 *** *** [0, 0.001]; ** [0.001, 0.01]; * [0.01, 0.05]; ∙ [0.05, 0.1]; [0.1, 1]

Table 6. The estimated regression coefficients in the fitted negative binomial regression model for the total number of insects.

Random Effects Groups Name Variance Std. Dev. Genus (Intercept) 2.40 1.55 Farm (Intercept) 0.06 0.25 Fixed Effects Parameter Estimate Std. Error (Intercept) −0.78 0.19 *** Zone:without −0.90 0.10 *** Year:2014 0.32 0.09 *** Year:2015 1.07 0.08 *** Zone:without-Year: 2014 0.84 0.13 *** Zone:without-Year: 2015 0.58 0.13 *** *** [0, 0.001]; ** [0.001, 0.01]; * [0.01, 0.05]; ∙ [0.05, 0.1]; [0.1, 1]

We finally performed hypotheses testing over the fixed and random effects to under- stand the behaviour of the total number of insects under the examined factors. For the fixed effects (zones and years), Tukey multiple comparisons strategy was used to compare the differences among the zones without and with cover plants through the years. Thus, for each year, we tested the null hypothesis that there is not a difference in the mean of the number of insects between the zone without cover plants and the zone with cover plants. The results are shown in Figure 4a and indicate that in the years 2013 and 2015, Insects 2021, 12, 740 there was a statistically significant difference, where the zone with cover plants had more 8 of 12 insects than the zone without cover plants on average.

(a) (b)

Figure 4.Figure Ninety-five 4. Ninety-five percent percentconfidence confidence intervals intervals for the fixed for the and fixed random and randomeffects of effects the fitted of the negative fitted negative binomial binomial GLMM; GLMM; (a) 95% (confidencea) 95% confidence intervals intervals for Tukey for Tukeymultiple multiple comparisons comparisons between between zones zonesthrough through years; years; (b) 95% (b) confidence 95% confidence intervals intervals for for the random intercepts of the farms. the random intercepts of the farms.

4. Discussion The importance of the use of cover plants to increase the diversity of plant species that directly or indirectly promote the diversity of invertebrates is well known [55,56]. Moreover, an increased mixture of plant species richness can potentially support increasing numbers of specialised feeders [55]. According to our first working hypothesis, this significant association between plants and insects can increase the number of species and individuals over time. Several studies have remarked the influence that cover plants have on the abundance of insects and their role in pest management [57–61]. The results of the present work show a similar pattern in the attraction of insects because in all farms, more species and individuals were observed in areas with cover plants than without them. However, the improvement obtained showed clear differences between Carpio and Sardón. These differences lie primarily in their location and the environmental conditions of the surrounding countryside. In the case of Carpio, the field is located in an arid zone without a nearby natural environment, while Sardón is near the stream of a river in an area of fluvial vegetation. This shows that the habitat surrounding the fields may play a very important role in the rate and speed of biodiversity enhancement. However, as noted in the current study, it is not a limiting factor. According to the second hypothesis, cover plants may contribute to the conservation of insects and biodiversity enhancement. The increase in vegetation structural complexity and diversity in vineyards is important to increased resources (i.e., pollen and nectar) or refuge in the field [62–64]. Numerous researchers have studied the effect of the management of cover plants in agricultural environments. However, these studies were based only on one growing season [57–61,65], unlike our study that analyzes the evolution of insect communities over three years. Looking only at the data obtained during the first year, we see that the results are very similar to previous studies, and a significant increase in the number of insects was observed. The analysis of three years data set showed a clear trend in the increase of the number of insects over the years in all the farms, both in the areas with plants and without plants. This increase in both areas may be due to the establishment of the durable cover plant over time, which plays a fundamental role in the creation of refuge areas and in the conservation of insects in the agricultural ecosystem. The only study we have been able to find that covers more than one crop season was carried out in an alfalfa field where a wildflower margin was used for three years [49]. The results of the alfalfa field also showed an increase in the number of insect species (102.47%) and individuals (97.64%) at the end of the experiment. These numbers are far from those observed in vineyards (12.10% species and 40.01% individuals), presumably because the difference in Insects 2021, 12, 740 9 of 12

the biology and flowering of crops, such as alfalfa flowers, attracts numerous pollinators and beneficials, while those of the vineyard do not. Furthermore, the enhancement of insect biodiversity due to cover plants may play an important role as a natural biological control. Therefore, its use is also recommended as an Integrated Pest Management (IPM) tool to balance and eventually reduces the long-term pest populations increasing the natural enemies’ community [62,66–69]. Our results showed that within the most abundant genera were Coccinella and Chrysoperla, who are predators of aphids, and Sphaerophoria, who perform both pollination and predation functions. These observations coincide with those obtained by other authors [57,59,60,70,71] and prove that cover plants are a great tool for IPM.

5. Conclusions The implementation of cover plants in agricultural landscapes results in biodiversity enhancements through the attraction of pollinators and beneﬁcial insects. These effects are mainly because cover plants create a perfect reservoir area with refuge and food (pollen, nectar, alternative prey) that allow them to maintain and extend their populations. In addition, we concluded that the implementation of permanent plant cover inter-row crops could be an important biological conservation strategy.

Supplementary Materials: The following are available online at https://www.mdpi.com/article/10 .3390/insects12080740/s1, Table S1: Dataset genus and individuals vineyards. Author Contributions: Conceptualization, F.J.P.-F. and F.S.; methodology, L.M.-B. and O.A.; software, F.S.; validation, J.V.F.-G., M.S. and A.I.; formal analysis, F.S.; investigation, F.J.P.-F. and O.A.; resources, V.V.; data curation, O.A., F.J.P.-F. and F.S.; writing—original draft preparation, F.J.P.-F., M.S. and F.S.; writing—review and editing, C.B.; visualization, F.J.P.-F.; supervision, L.M.-B.; project administration, V.V.; funding acquisition, V.V. All authors have read and agreed to the published version of the manuscript. Funding: This research was funded by the project Operation Pollinator (Syngenta). Institutional Review Board Statement: Not applicable. Informed Consent Statement: Not applicable. Data Availability Statement: The data presented in this study are available from the corresponding author, upon reasonable request. Acknowledgments: We are very grateful to the staff of Abadía Retuerta (Sardón de Duero, Val- ladolid), Área Tudanca (Aranda del Duero, Burgos), Casas de Hualdo (El Carpio de Tajo, Toledo), Dehesa de Valquejigoso (Villamanta, Madrid) and Finca Constancia (Otero, Toledo) for their kindness and support during our research. Moreover, we want to thank Francisco “Paco” García Verde for his involvement and work in the launch and development of the study. This research was funded by the project Operation Pollinator (Syngenta). Conﬂicts of Interest: The authors declare no conﬂict of interest.

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